A comprehensive multivariate model of biopsychosocial factors associated with opioid misuse and use disorder in a 2017–2018 United States national survey

Abstract

Background: Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors. Methods: A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older (N = 85,580; weighted N = 248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives). Results: The comprehensive model found that criminality (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI] = 1.98–3.37, p < 0.001), self-reported health (i.e., excellent compared to fair/poor [AOR = 3.71, 95% CI = 2.19–6.29, p < 0.001], good [AOR = 3.43, 95% CI = 2.20–5.34, p < 0.001], and very good [AOR = 2.75, 95% CI = 1.90–3.98, p < 0.001]), no private health insurance (AOR = 2.12, 95% CI = 1.55–2.89, p < 0.001), serious psychological distress (AOR = 2.12, 95% CI = 1.55–2.89, p < 0.001), suicidality (AOR = 1.58, 95% CI = 1.17–2.14, p = 0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR = 3.01, 95% CI = 2.30–3.93, p < 0.001), alcohol (AOR = 1.40, 95% CI = 1.02–1.92, p = 0.038), marijuana (AOR = 2.24, 95% CI = 1.40–3.58, p = 0.001), cocaine (AOR = 3.92, 95% CI = 2.14–7.17, p < 0.001), methamphetamine (AOR = 3.32, 95% CI = 1.96–5.64, p < 0.001), tranquilizers (AOR = 16.72, 95% CI = 9.75–28.65, p < 0.001), and stimulants (AOR = 2.45, 95% CI = 1.03–5.87, p = 0.044). Conclusions: Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics.